Scene Parsing

The Segmentation of Underwater IMagery (SUIM) dataset contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants. Source: Semantic Segmentation of Underwater Imagery: Dataset and Benchmark Image Source: http://irvlab.cs.umn.edu/resources/suim-dataset

PSG dataset has 48749 images with 133 object classes (80 objects and 53 stuff) and 56 predicate classes. It annotates inter-segment relations based on COCO panoptic segmentation.

The SensatUrbat dataset is an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is five times the number of labeled points than the existing largest point cloud dataset. The dataset consists of large areas from two UK cities, covering about 6 km^2 of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes, such as ground, vegetation, car, etc.. Source: https://github.com/QingyongHu/SensatUrban Image Source: https://github.com/QingyongHu/SensatUrban

RADIATE (RAdar Dataset In Adverse weaThEr) is new automotive dataset created by Heriot-Watt University which includes Radar, Lidar, Stereo Camera and GPS/IMU. The data is collected in different weather scenarios (sunny, overcast, night, fog, rain and snow) to help the research community to develop new methods of vehicle perception. The radar images are annotated in 7 different scenarios: Sunny (Parked), Sunny/Overcast (Urban), Overcast (Motorway), Night (Motorway), Rain (Suburban), Fog (Suburban) and Snow (Suburban). The dataset contains 8 different types of objects (car, van, truck, bus, motorbike, bicycle, pedestrian and group of pedestrians). Source: https://github.com/marcelsheeny/radiate_sdk Image Source: https://github.com/marcelsheeny/radiate_sdk

Toronto-3D is a large-scale urban outdoor point cloud dataset acquired by an MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of road and consists of about 78.3 million points. Point clouds has 10 attributes and classified in 8 labelled object classes. Source: https://github.com/WeikaiTan/Toronto-3D Image Source: https://github.com/WeikaiTan/Toronto-3D

A simulation-based dataset featuring 20,000 stack configurations composed of a variety of elementary geometric primitives richly annotated regarding semantics and structural stability. Source: ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking

Features a large-scale dataset with 12,263 annotated images. Two tasks, namely text localization and end-to-end recognition, are set up. The competition took place from January 20 to May 31, 2017. 23 valid submissions were received from 19 teams. Source: ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)

The heavily occluded scene text (HOST) dataset is a dataset that contains images of text with occlusions. It is used to improve the recognition performance of occluded text in machine vision applications 1. The dataset is composed of 4832 images that are manually occluded in weak or heavy degrees.

The CUTE80 dataset is a lightweight collection of images specifically designed for text detection in natural scene images. It contains a total of 13,000 annotated page images across five different popular categories: 1) Table 2) Figure 3) Natural image 4) Logo 5) ignature

MLRSNet is a a multi-label high spatial resolution remote sensing dataset for semantic scene understanding. It provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation. Source: https://github.com/cugbrs/MLRSNet Image Source: Qi et al

SceneNet is a dataset of labelled synthetic indoor scenes. There are several labeled indoor scenes, including: - 11 Bedroom scenes with 428 objects - 15 Office scenes with 1,203 objects - 11 Kitchen scenes with 797 objects - 10 Living Room scenes with 715 objects - 10 Bathrooms with 556 objects Source: https://robotvault.bitbucket.io/ Image Source: https://robotvault.bitbucket.io/big_scene.html

The Weakly Occluded Scene Text (WOST) dataset is a public dataset for scene text segmentation. It is used to generate pixel-level annotations in scene text images 1. The dataset is designed to contain weakly annotated images, which means that the images are not fully annotated with pixel-level labels.

DADA-2000 is a large-scale benchmark with 2000 video sequences (named as DADA-2000) is contributed with laborious annotation for driver attention (fixation, saccade, focusing time), accident objects/intervals, as well as the accident categories, and superior performance to state-of-the-arts are provided by thorough evaluations. Source: DADA: A Large-scale Benchmark and Model for Driver Attention Prediction in Accidental Scenarios Image Source: Fang et al

A novel benchmark dataset that includes a manually annotated point cloud for over 260 million laser scanning points into 100'000 (approx.) assets from Dublin LiDAR point cloud [12] in 2015. Objects are labelled into 13 classes using hierarchical levels of detail from large (i.e., building, vegetation and ground) to refined (i.e., window, door and tree) elements. Source: DublinCity: Annotated LiDAR Point Cloud and its Applications

This dataset includes 4,500 fully annotated images (over 30,000 license plate characters) from 150 vehicles in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving. The images were acquired with three different cameras and are available in the Portable Network Graphics (PNG) format with a size of 1,920 × 1,080 pixels. The cameras used were: GoPro Hero4 Silver, Huawei P9 Lite, and iPhone 7 Plus. We collected 1,500 images with each camera, divided as follows: - 900 of cars with gray license plates; - 300 of cars with red license plates; - 300 of motorcycles with gray license plates. The dataset is split as follows: 40% for training, 40% for testing and 20% for validation. Every image has the following annotations available in a text file: the camera in which the image was taken, the vehicle’s position and information such as type (car or motorcycle), manufacturer, model and year; the identification and position of the license plate, as well as the position of its characters. Source: A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

The IIIT5K dataset contains 5,000 text instance images: 2,000 for training and 3,000 for testing. It contains words from street scenes and from originally-digital images. Every image is associated with a 50 -word lexicon and a 1,000 -word lexicon.

SVTP dataset stands for Scene Text Recognition Datasets. It is a collection of 4 popular Latin/English scene text recognition datasets, namely IIIT5K, SVT, SVTP, and CUTE-80. These datasets only provide case-insensitive annotations and no punctuation marks.

A new and challenging video database of dynamic scenes that more than doubles the size of those previously available. This dataset is explicitly split into two subsets of equal size that contain videos with and without camera motion to allow for systematic study of how this variable interacts with the defining dynamics of the scene per se. Source: Temporal Residual Networks for Dynamic Scene Recognition

IRS is an open dataset for indoor robotics vision tasks, especially disparity and surface normal estimation. It contains totally 103,316 samples covering a wide range of indoor scenes, such as home, office, store and restaurant. Source: https://github.com/HKBU-HPML/IRS Image Source: https://github.com/HKBU-HPML/IRS

4D-OR includes a total of 6734 scenes, recorded by six calibrated RGB-D Kinect sensors 1 mounted to the ceiling of the OR, with one frame-per-second, providing synchronized RGB and depth images. We provide fused point cloud sequences of entire scenes, automatically annotated human 6D poses and 3D bounding boxes for OR objects. Furthermore, we provide SSG annotations for each step of the surgery together with the clinical roles of all the humans in the scenes, e.g., nurse, head surgeon, anesthesiologist.

Dataset Description The Greek Sign Language (GSL) is a large-scale RGB+D dataset, suitable for Sign Language Recognition (SLR) and Sign Language Translation (SLT). The video captures are conducted using an Intel RealSense D435 RGB+D camera at a rate of 30 fps. Both the RGB and the depth streams are acquired in the same spatial resolution of 848×480 pixels. To increase variability in the videos, the camera position and orientation is slightly altered within subsequent recordings. Seven different signers are employed to perform 5 individual and commonly met scenarios in different public services. The average length of each scenario is twenty sentences. The dataset contains 10,290 sentence instances, 40,785 gloss instances, 310 unique glosses (vocabulary size) and 331 unique sentences, with 4.23 glosses per sentence on average. Each signer is asked to perform the pre-defined dialogues five consecutive times. In all cases, the simulation considers a deaf person communicating with a single public service employee. The involved signer performs the sequence of glosses of both agents in the discussion. For the annotation of each gloss sequence, GSL linguistic experts are involved. The given annotations are at individual gloss and gloss sequence level. A translation of the gloss sentences to spoken Greek is also provided. ## Evaluation The GSL dataset includes the 3 evaluation setups: - Signer-dependent continuous sign language recognition (GSL SD) – roughly 80% of videos are used for training, corresponding to 8,189 instances. The rest 1,063 (10%) were kept for validation and 1,043 (10%) for testing. - Signer-independent continuous sign language recognition (GSL SI) – the selected test gloss sequences are not used in the training set, while all the individual glosses exist in the training set. In GSL SI, the recordings of one signer are left out for validation and testing (588 and 881 instances, respectively). The rest 8821 instances are utilized for training. - Isolated gloss sign language recognition (GSL isol.) – The validation set consists of 2,231 gloss instances, the test set 3,500, while the remaining 34,995 are used for training. All 310 unique glosses are seen in the training set. For more info and results, advice our paper ## Paper Abstract: A Comprehensive Study on Sign Language Recognition Methods, Adaloglou et al. 2020 In this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted. By implementing the most recent deep neural network methods in this field, a thorough evaluation on multiple publicly available datasets is performed. The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses. For this task, two new sequence training criteria, known from the fields of speech and scene text recognition, are introduced. Furthermore, a plethora of pretraining schemes are thoroughly discussed. Finally, a new RGB+D dataset for the Greek sign language is created. To the best of our knowledge, this is the first sign language dataset where sentence and gloss level annotations are provided for every video capture. Arxiv link

A large-scale Landmark guided face Parsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks. Source: LaPa

DAWN emphasizes a diverse traffic environment (urban, highway and freeway) as well as a rich variety of traffic flow. The DAWN dataset comprises a collection of 1000 images from real-traffic environments, which are divided into four sets of weather conditions: fog, snow, rain and sandstorms. The dataset is annotated with object bounding boxes for autonomous driving and video surveillance scenarios. This data helps interpreting effects caused by the adverse weather conditions on the performance of vehicle detection systems. Source: DAWN: Vehicle Detection in Adverse Weather Nature Dataset Image Source: Kenk and Hassaballah

DeepScores contains high quality images of musical scores, partitioned into 300,000 sheets of written music that contain symbols of different shapes and sizes. For advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding. Source: DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects Image Source: https://tuggeluk.github.io/deepscores/

ImageNet-VidVRD dataset contains 1,000 videos selected from ILVSRC2016-VID dataset based on whether the video contains clear visual relations. It is split into 800 training set and 200 test set, and covers common subject/objects of 35 categories and predicates of 132 categories. Ten people contributed to labeling the dataset, which includes object trajectory labeling and relation labeling. Since the ILVSRC2016-VID dataset has the object trajectory annotation for 30 categories already, we supplemented the annotations by labeling the remaining 5 categories. In order to save the labor of relation labeling, we labeled typical segments of the videos in the training set and the whole of the videos in the test set.

The AIRS (Aerial Imagery for Roof Segmentation) dataset provides a wide coverage of aerial imagery with 7.5 cm resolution and contains over 220,000 buildings. The task posed for AIRS is defined as roof segmentation. Source: Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings

Context This is the Original data provided by MIT . Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g., bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information. Content The database contains 67 Indoor categories, and a total of 15620 images. The number of images varies across categories, but there are at least 100 images per category. All images are in jpg format. The images provided here are for research purposes only. Acknowledgements Thanks to MIT Thanks to Aude Oliva for helping to create the database of indoor scenes. Funding for this research was provided by NSF Career award (IIS 0747120)

The Pascal Panoptic Parts dataset consists of annotations for the part-aware panoptic segmentation task on the PASCAL VOC 2010 dataset. It is created by merging scene-level labels from PASCAL-Context with part-level labels from PASCAL-Part Source: https://arxiv.org/abs/2106.06351 Image Source: https://github.com/tue-mps/panoptic_parts

PSI-AVA is a dataset designed for holistic surgical scene understanding. It contains approximately 20.45 hours of the surgical procedure performed by three expert surgeons and annotations for both long-term (Phase and Step recognition) and short-term reasoning (Instrument detection and novel Atomic Action recognition) in robot-assisted radical prostatectomy videos. Source: Towards Holistic Surgical Scene Understanding Image Source: https://arxiv.org/pdf/2212.04582v2.pdf

The Cityscapes Panoptic Parts dataset introduces part-aware panoptic segmentation annotations for the Cityscapes dataset. It extends the original panoptic annotations for the Cityscapes dataset with part-level annotations for selected scene-level classes. Source: https://arxiv.org/pdf/2004.07944.pdf Image Source: https://github.com/pmeletis/panoptic_parts

DeepLoc is a large-scale urban outdoor localization dataset. The dataset is currently comprised of one scene spanning an area of 110 x 130 m, that a robot traverses multiple times with different driving patterns. The dataset creators use a LiDAR-based SLAM system with sub-centimeter and sub-degree accuracy to compute the pose labels that provided as groundtruth. Poses in the dataset are approximately spaced by 0.5 m which is twice as dense as other relocalization datasets. Furthermore, for each image the dataset creators provide pixel-wise semantic segmentation annotations for ten categories: Background, Sky, Road, Sidewalk, Grass, Vegetation, Building, Poles & Fences, Dynamic and Void. The dataset is divided into a train and test splits such that the train set comprises seven loops with alternating driving styles amounting to 2737 images, while the test set comprises three loops with a total of 1173 images. The dataset also contains global GPS/INS data and LiDAR measurements. This dataset can be very challenging for vision based applications such as global localization, camera relocalization, semantic segmentation, visual odometry and loop closure detection, as it contains substantial lighting, weather changes, repeating structures, reflective and transparent glass buildings. Source: http://deeploc.cs.uni-freiburg.de/ Image Source: http://deeploc.cs.uni-freiburg.de/

This dataset, called RodoSol-ALPR dataset, contains 20,000 images captured by static cameras located at pay tolls owned by the Rodovia do Sol (RodoSol) concessionaire, which operates 67.5 kilometers of a highway (ES-060) in the Brazilian state of Espírito Santo. There are images of different types of vehicles (e.g., cars, motorcycles, buses and trucks), captured during the day and night, from distinct lanes, on clear and rainy days, and the distance from the vehicle to the camera varies slightly. All images have a resolution of 1,280 × 720 pixels. An important feature of the proposed dataset is that it has images of two different license plate (LP) layouts: Brazilian and Mercosur (to maintain consistency with existing works, we refer to “Brazilian” as the standard used in Brazil before the adoption of the Mercosur standard). Every image has the following information available in a text file: the vehicle’s type (car or motorcycle), the LP’s layout (Brazilian or Mercosul), its text (e.g., ABC-1234), and the position (x, y) of each of its four corners. We labeled the corners instead of just the LP bounding box to enable the training of methods that explore LP rectification, as well as the application of a wider range of data augmentation techniques. Regarding privacy concerns related to our dataset, we remark that in Brazil the LPs are related to the respective vehicles, i.e., no public information is available about the vehicle drivers/owners. Moreover, all human faces (e.g., drivers or RodoSol’s employees) were manually redacted (i.e., blurred) in each image.

This dataset aims at evaluating the License Plate Character Segmentation (LPCS) problem. The experimental results of the paper Benchmark for License Plate Character Segmentation were obtained using a dataset providing 101 on-track vehicles captured during the day. The video was recorded using a static camera in early 2015. The images of the dataset were acquired with a digital camera in Full-HD and are available in the Portable Network Graphics (PNG) format with 1920×1080 pixels each. The average size of each file is 4.08 Megabytes (a total of 8.60 Gigabytes for the entire dataset). In addition, since there are some approaches that track the car to utilize redundant information to improve the recognition results, we decided to make a dataset with multiples frames per car. In this dataset, there are, on average, 19.80 image frames per vehicle (with a standard deviation of 4.14). Source: Benchmark for License Plate Character Segmentation

SynPick is a synthetic dataset for dynamic scene understanding in bin-picking scenarios. In contrast to existing datasets, this dataset is both situated in a realistic industrial application domain -- inspired by the well-known Amazon Robotics Challenge (ARC) -- and features dynamic scenes with authentic picking actions as chosen by our picking heuristic developed for the ARC 2017. The dataset is compatible with the popular BOP dataset format. The dataset consists of 21 Synthetic videos with 503,232 with diverse lightning and 3 different views of each video.

The AuDio Visual Aerial sceNe reCognition datasEt (ADVANCE) is a brand-new multimodal learning dataset, which aims to explore the contribution of both audio and conventional visual messages to scene recognition. This dataset in summary contains 5075 pairs of geotagged aerial images and sounds, classified into 13 scene classes, i.e., airport, sports land, beach, bridge, farmland, forest, grassland, harbor, lake, orchard, residential area, shrub land, and train station. Source:

IIIT-ILST is a dataset and benchmark for scene text recognition for three Indic scripts - Devanagari, Telugu and Malayalam. IIIT-ILST contains nearly 1000 real images per each script which are annotated for scene text bounding boxes and transcriptions.